基于Sentinel-2多光谱数据的棉花叶面积指数估算

棉花叶面积指数(leaf are index, LAI)的快速、准确获取对棉花长势监测、发育期诊断、面积提取以及产量估算等遥感监测具有重要意义。该研究利用2017年和2018年的Sentinel-2多光谱卫星数据及大面积田间试验观测获取的棉花不同发育期LAI实测数据,构建了基于单波段反射率及各类植被指数的棉花不同发育期及全发育期LAI估算模型,并采用留一验证(LOOCV, leave-one-out cross validation)和交叉验证对模型精度进行了检验。结果表明:1)对于单波段反射率,基于中心波长为842 nm波宽为145 nm的B8近红外波段对不同发育期LAI估算精度最优均方根误差(RMSE, root mean square error, RMSE=0.378);2)对于各类植被指数,花蕾期(20170616)和花铃期(20170802)时增强植被指数(EVI, enhanced vegetation index,)表现最佳(RMSE分别为0.352和0.367),开花期(20180623)时校正土壤调节植被指数(MSAVI2, modified soil adjusted vegetation index 2,)估算精度最高(RMSE=0.323);3)单波段反射率和各类植被指数对全发育期LAI的估算均要优于对单个发育期LAI的估算,其中基于IRECI指数的(invertedred-edge chlorophyllindex)全发育期LAI估算模型精度最佳,LOOCV检验RMSE=0.425,交叉检验RMSE=0.368;将基于IRECI的全发育期LAI估算模型应用到单个发育期LAI估算并与各单个发育期LAI估算模型精度对比,发现交叉验证RMSE平均值仅比LOOCV验证RMSE平均值高0.07,反映了全发育期LAI估算模型良好的普适性。该研究为农作物LAI估算提供了新的数据选择,完善了Sentinel-2卫星数据在LAI估算中的应用领域。

Remote estimation of cotton LAI using Sentinel-2 multispectral data

Rapid and accurate LAI (Leaf Area Index) acquisition is of great significance for remote sensing monitoring of cotton growth, diagnosis of growth stage, extraction of cotton plant area and yield estimation. The present research discussed the characteristics of Sentinel-2 multi-spectral satellite data for remote estimation of cotton LAI. Measured LAI from filed experiments and Sentinel-2 data in 2017 and 2018 were obtained, and LAI estimation model for different and for all growth stages were established basing on single spectral band reflectance on Sentinel-2 and various vegetation index from Sentinel-2 bands. The estimation accuracy of the established LAI models were validated by coefficient of determination (R2), RMSE (root mean square error), mean bias, and slope and intercept, using LOOCV (Leave-One-Out-Cross Validation) method and cross validation, respectively. The results showed that: 1) for the single-band reflectance of sentinel-2 multi-spectral satellite data, two red-edge bands of B6 and B7, and two near-infrared bands of B8 and B8a, were all significantly (P<0.001) correlated to LAI at all three tested growth stages, i.e. bud stage (16-Jun-2017), and flowering stage (23-Jun-2018), and boll stage (2-Aug-2017), with correlation coefficient greater than 0.7. And when the correlation between LAI and band reflectance were performed using data consist of three growth stages, the correlation coefficient for all tested bands reach significant level (P<0.001), and the maximum correlation coefficient was 0.943 of near-infrared narrow band B8a, which center at 865 nm with a wave width of 32 nm. The accuracy of LAI estimation at different development stages was optimized using the near-infrared band B8 which with a central wavelength of 842 nm and a wave width of 145 nm, with all RMSE smaller than 0.465. 2) for seventeen LAI related vegetation indices, including EVI (Enhanced Vegetation Index), MSAVI2 (Modified Soil Adjusted Vegetation Index 2), IRECI (Inverted Red-Edge Chlorophyll Index), etc., most of them were significantly (P<0.001) correlated with LAI, especially atmospheric correction index EVI, soil adjusted index MSAVI2, and red-edge index IRECI, and the coefficient of correlation were over 0.8. EVI provided the best result for LAI estimation at bud stage and boll stage, and at flowering stage it consists by MASVI2, with bud stage RMSE=0.352, and boll stage RMSE=0.367 and flowering stage RMSE=0.323, respectively. 3) LAI estimation models for whole growth stages performed better than these for one single growth stage. And the best LAI estimation models for whole growth period using single spectral band reflectance and vegetation index were respectively obtained by near-infrared narrow band B8a and IRECI, with IRECI performed slightly better, which with R2=0.908 and RMSE=0.425 for LOOCV, and R2=0.951 and RMSE=0.368 for cross validation. Additionally, when apply IRECI-LAI estimation model for whole growth stages on one single growth stage LAI estimation, the accuracy comparison between the IRECI-LAI model and single growth stage LAI models showed that the average cross validation RMSE was only 0.07 greater than the average LOOCV RMSE, indicating the good universality of LAI estimation model for whole growth stages.